In Detail

Machine learning, the field of building systems that learn from data, is exploding on the Web and elsewhere. Python is a wonderful language in which to develop machine learning applications. As a dynamic language, it allows for fast exploration and experimentation and an increasing number of machine learning libraries are developed for Python.

Building Machine Learning system with Python shows you exactly how to find patterns through raw data. The book starts by brushing up on your Python ML knowledge and introducing libraries, and then moves on to more serious projects on datasets, Modelling, Recommendations, improving recommendations through examples and sailing through sound and image processing in detail.

Using open-source tools and libraries, readers will learn how to apply methods to text, images, and sounds. You will also learn how to evaluate, compare, and choose machine learning techniques.

Written for Python programmers, Building Machine Learning Systems with Python teaches you how to use open-source libraries to solve real problems with machine learning. The book is based on real-world examples that the user can build on.

Readers will learn how to write programs that classify the quality of StackOverflow answers or whether a music file is Jazz or Metal. They will learn regression, which is demonstrated on how to recommend movies to users. Advanced topics such as topic modeling (finding a text's most important topics), basket analysis, and cloud computing are covered as well as many other interesting aspects.

Building Machine Learning Systems with Python will give you the tools and understanding required to build your own systems, which are tailored to solve your problems.

Approach

A practical, scenario-based tutorial, this book will help you get to grips with machine learning with Python and start building your own machine learning projects. By the end of the book you will have learnt critical aspects of machine learning Python projects and experienced the power of ML-based systems by actually working on them.

Who this book is for

This book is for Python programmers who are beginners in machine learning, but want to learn Machine learning. Readers are expected to know Python and be able to install and use open-source libraries. They are not expected to know machine learning, although the book can also serve as an introduction to some Python libraries for readers who know machine learning. This book does not go into the detail of the mathematics behind the algorithms.

This book primarily targets Python developers who want to learn and build machine learning in their projects, or who want to provide machine learning support to their existing projects, and see them getting implemented effectively.

Not happy with the product as in my opinion it has not been properly written i.e. seems to have been written in a hurry and, although you would not expect too many details, in several cases it seems too superficial. Writing a book which is intended as a good introduction should be very careful in conveying ideas properly.

A must-read book those who enjoy programming in python and want to get into machine learning. The book contains great examples; it's well written and easy to understand/follow. The book gives a good introduction to many ML tools out there.

I think it's a great book for people like me. I'm but no means a data scientist: I just sometimes undertake some data analysis, usually looking for a pattern for a bug or performance problem. I know the basics since I have taken a couple coursera courses(ML& NLP from Stanford) and have read some other introductory ML books.

The MOOCS and most books focus on algorithms and why the they work. Other books (like Data Science for Business or Bad Data) try to teach the what 'sand what not's of the whole process. But it's the first book I have found that actually accompanies every step with the actual code.

Coding your own (horribly inefficient and broken) Naive Bayes implementation is nice and a necessary step while learning ML, but what you need in real life is an example of parsing a corpus, stemming it and throwing it against an efficiently coded model and this book shows how to do it, justifying every path taken.

May be ML for hackers is comparable to this one, but haven't read it since uses R, which I've found hard to learn and foreign.

Definitely I recommend it. I give it 5 stars since delivers what promises.

I have been interested in machine learning for quite a while now but most of the texts I have come across have been fairly dry and often either connected to a language in which I can't programme or not connected to one at all taking a rather abstract approach.

"Building Machine Learning Systems with Python" is an excellent exception. For a beginner this book is perfect. The only assumption the author's make is that you know your way around Python and that it is installed on your machine (which is a fairly safe bet if you pick up the book).

The book itself is split into 12 chapters.

The first chapter eases you in to the subject with an introduction to some of the tools and libraries (numpy, scipy, etc.) you will use and even a first, albeit small, machine learning application.

Chapter 2 deals with classification, describing how to visualise the data and build a model.

Chapter 3 introduces clustering and offers examples both on how and how not to group data particularly text. It also includes a brief introduction to the Native Language Toolkit (NLTK).

Topic modelling is discussed in the fourth chapter. Here we are also introduced to new terms and tools.

Chapter 5 takes us back to classification. Here we learn to differentiate between 'good' and 'bad' answers and such topics as bias and variance.

In chapter 6 we look at sentiment analysis, using twitter as an example, and we get to know a little about Bayes Theorem.

Classification rears its head again in chapter 9. This was one of the most difficult chapters for me to follow although extremely interesting. The example is music genre classification and can be quite mathematically intense.

Chapter 10 is all about computer vision and pattern recognition, we touch on image processing and dealing with noise.

Chapter 11 helps to break down the data making it easier to process.

And chapter 12 brings it all together discussing big data, multi-core processing and even an intro to AWS. We are encouraged to use that which we have learned with interesting examples.

All in all I found this a terrific introduction. The book's conversational tone and interesting teaching style make for a great read. I do feel that I have a good base for learning more. I can't recommend this book enough.